Noa Zilberman

Bio: Noa Zilberman is an academic researcher from University of Oxford. The author has contributed to research in topics: Networking hardware & Computer science. The author has an hindex of 18, co-authored 59 publications receiving 1129 citations. Previous affiliations of Noa Zilberman include University of Cambridge & Tel Aviv University.

NetFPGA SUME: Toward 100 Gbps as Research Commodity

Abstract: The demand-led growth of datacenter networks has meant that many constituent technologies are beyond the research community's budget. NetFPGA SUME is an FPGA-based PCI Express board with I/O capabilities for 100 Gbps operation as a network interface card, multiport switch, firewall, or test and measurement environment. NetFPGA SUME provides an accessible development environment that both reuses existing codebases and enables new designs.

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Abstract: With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

A Geolocation Databases Study

Abstract: The geographical location of Internet IP addresses is important for academic research, commercial and homeland security applications. Thus, both commercial and academic databases and tools are available for mapping IP addresses to geographic locations. Evaluating the accuracy of these mapping services is complex since obtaining diverse large scale ground truth is very hard. In this work we evaluate mapping services using an algorithm that groups IP addresses to PoPs, based on structure and delay. This way we are able to group close to 100,000 IP addresses world wide into groups that are known to share a geo-location with high confidence. We provide insight into the strength and weaknesses of IP geolocation databases, and discuss their accuracy and encountered anomalies.

Do Switches Dream of Machine Learning?: Toward In-Network Classification

Abstract: Machine learning is currently driving a technological and societal revolution. While programmable switches have been proven to be useful for in-network computing, machine learning within programmable switches had little success so far. Not using network devices for machine learning has a high toll, given the known power efficiency and performance benefits of processing within the network. In this paper, we explore the potential use of commodity programmable switches for in-network classification, by mapping trained machine learning models to match-action pipelines. We introduce IIsy, a software and hardware based prototype of our approach, and discuss the suitability of mapping to different targets. Our solution can be generalized to additional machine learning algorithms, using the methods presented in this work.

The P4->NetFPGA Workflow for Line-Rate Packet Processing

Abstract: P4 has emerged as the de facto standard language for describing how network packets should be processed, and is becoming widely used by network owners, systems developers, researchers and in the classroom. The goal of the work presented here is to make it easier for engineers, researchers and students to learn how to program using P4, and to build prototypes running on real hardware. Our target is the NetFPGA SUME platform, a 4x10 Gb/s PCIe card designed for use in universities for teaching and research. Until now, NetFPGA users have needed to learn an HDL such as Verilog or VHDL, making it off limits to many software developers and students. Therefore, we developed the P4->NetFPGA workflow, allowing developers to describe how packets are to be processed in the high-level P4 language, then compile their P4 programs to run at line rate on the NetFPGA SUME board. The P4->NetFPGA workflow is built upon the Xilinx P4-SDNet compiler and the NetFPGA SUME open source code base. In this paper, we provide an overview of the P4 programming language and describe the P4->NetFPGA workflow. We also describe how the workflow is being used by the P4 community to build research prototypes, and to teach how network systems are built by providing students with hands-on experience working with real hardware.

Software-Defined Networking: A Comprehensive Survey

Abstract: The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

Software-Defined Networking: A Comprehensive Survey

Abstract: Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

The Internet Topology Zoo

Abstract: The study of network topology has attracted a great deal of attention in the last decade, but has been hampered by a lack of accurate data. Existing methods for measuring topology have flaws, and arguments about the importance of these have overshadowed the more interesting questions about network structure. The Internet Topology Zoo is a store of network data created from the information that network operators make public. As such it is the most accurate large-scale collection of network topologies available, and includes meta-data that couldn't have been measured. With this data we can answer questions about network structure with more certainty than ever before - we illustrate its power through a preliminary analysis of the PoP-level topology of over 140 networks. We find a wide range of network designs not conforming as a whole to any obvious model.

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